1

Computational Viability of Fog Methodologies in IoT Enabled Smart City

Architectures-A Smart Grid Case Study

Md. Muzakkir Hussain1,*

, Mohammad Saad Alam2, M.M. Sufyan Beg

1

1Department of Computer Engineering, ZHCET, AMU

2Department of Computer Engineering, ZHCET, AMU

Abstract

The gradual evolution in the Information Communication Technology (ICT) support of Smart City (SC) architecture

leveraged with meshes of Internet of Things (IoT) creates and welcomes research and investment efforts from academia,

R&Ds and policymakers. The IoT utilities are being deployed at every layers of a typical SG backbone namely Application

layer, Energy layer and Communication layer. The geo-distributed clusters of IoT ―objects‖ produce galactic volume of data

that exacerbates the need to make a paradigm shift from centralized data center based processing to a hybrid model that

supports both in situ as well as cloud based storage and computational resources. To combat such SC issues, Fog Computing

(FC) emerges as a promising solution, which pushes the computation resources onto the network edge nodes. This work

investigates the high performance of Fog Computing over generic cloud computing in terms of metrics viz. latencies, power

consumption etc, through a Smart Grid (SG) use-case. Through an operational cost optimization framework, the work

comprehends the suitability of fog methodologies to make a synergistic interplay with the core centered clouds thus

empowering a wide breed of real-time and latency free services. Finally, an overview of the core orchestration issues,

challenges, and future research directions are presented for FC enabled SCs.

Keywords: Smart Cities (SC), Smart Grid (SG), Internet of Things (IoT), Fog computing, Cloud computing, Differential Evolution (DE)

Received on 09 December 2017, accepted on 21 December 2017, published on 12 February 2018

Copyright © 2018 Md. Muzakkir Hussain et al., licensed to EAI. This is an open access article distributed under the terms of the

Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and

reproduction in any medium so long as the original work is properly cited.

doi: 10.4108/eai.12-2-2018.154104

1. Introduction

Smart city vision brings emerging heterogeneous

communication technologies such as Fog Computing (FC)

together to substantially reduce the latency and energy

consumption of Internet of Everything (IoE) devices running

various applications. The key feature that distinguishes the

FC paradigm for smart cities is that it spreads communication

and computing resources over the wired/wireless access

network (e.g., proximate access points and base stations) to

provide resource augmentation (e.g., cyber-foraging) for

resource- and energy-limited wired/wireless (possibly mobile)

things. Moreover, smart city applications are developed with

the goal of improving the management of urban flows and

allowing real-time responses to challenges that can arise in

users’ transactional relationships.

The notion of smart city (SC) arises from the concept of

efficient utilization of city resources for enhancing quality of

life of inhabiting citizens, leading to acceleration of urban

penetration. For ensuring an enhanced standard of living,

utilities should focus on improvement of services and

infrastructure in such cities. Thanks to the revolution in

Information and Communication Technology (ICT) and the

power of the Internet, the contemporary infrastructures and

public services are expected to be more interactive, more

accessible, and more efficient while stepping towards the

realization of smart cities. In lieu of such domains, the

emergence of the Internet of Things (IoT) paradigm strongly

encourages utilization of the IoT’s potential to support the

smart city vision around the globe. As a consequence, the

smart city has emerged as one of the important application

drivers for IoT aided services. IoT enabled SC architectures

promote the concept of interrelated physical objects (things),

uniquely identified and distributed over broad physical areas

covering entire city geography.

Recently, the IoT technologies have stepped forward towards

connecting five pillars viz. ―things‖, data, process, energy and

people, forming the Internet of Everything (IoE)

environments. From one perspective, cities can be regarded as

an aggregation of interconnected networks that make up the

IoE. Hence, the IoE pillars play a significant role and work

together toward the promise of our smart city vision for the

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12 2017 - 02 2018 | Volume 2 | Issue 7 | e3

∗Corresponding author. Email: md.muzakkirhussain@zhcet.ac.in

future. The IoE’s creation of data deluge synonymously Big

Data (BD) over a distributed environment has the potential to

create processing as well as storage concerns for such data-

streams. Although, Cloud Computing posits to address such

problems providing virtually unlimited and flexible resource

pools but cannot work efficiently for mission critical Smart

City applications due to its inherent problems. For instance,

smart city applications like health monitoring, traffic

monitoring, power transmission networks of smart grid

systems etc, cannot tolerate the delay and latency incurred

when transferring a massive amount of data to the remote

Cloud Computing center and then back to the application.

The concept of Fog Computing (FC) had recently been

proposed here, as an architectural set-up that extends Cloud

services to the edge of the network, closer to the end user,

which reduces data processing time and network traffic

overhead. The primary definition of FC was introduced by

Cisco as ―an architecture that uses one or a collaborative

multitude of end-user clients or near-user edge devices to

carry out a substantial amount of storage (rather than stored

primarily in cloud data centers), communication (rather than

routed over the internet backbone), and control,

configuration, measurement and management (rather than

controlled primarily by network gateways such as those in the

LTE (telecommunication) core)‖. The most fundamental entity

in FC, called a Fog Computing Node (FCN), facilitates the

execution of IoT applications. Basically, FC can act as an

interface layer between end users end devices and distant

Cloud data centers, with the aim of satisfying mobility

support, locational awareness, geo distribution, and low

latency requirements for IoT applications.

In future SCs, the SG will be critical in ensuring reliability,

availability, and efficiency in city-wide electricity

management. Figure 6 demonstrates an example future smart

grid system, where Fog/Cloud computing can play a

significant role. A successful smart grid system will be able to

help improve transmission efficiency of electricity, react and

restore timely after power disturbances, reduce operation and

management costs, better integrate renewable energy systems,

effectively save electricity for future usage, and so on. It will

also be critical in building better electricity networks to help

bring down electricity bills and balance the whole electricity

system. In addition, the smart power grid system should

monitor power generation, power demands and help make

storage decisions. In terms of security, a smarter grid will also

add resiliency to large-scale electric power systems so as to

help governments react promptly to emergencies or natural

disasters, e.g., severe storms, earthquakes, large solar flares,

and even terrorist attacks, etc.

Motivated by those considerations, we present FC supported

Smart Grid (SG) use-case to investigate the expediencies of

FC paradigms towards fulfilling store and compute

requirements of emerging SC services. A multitier FC

structure in SG supports the applications running on things to

jointly compute, route, and communicate with one another

through the IoE environment to decrease latency and improve

energy provisioning and the efficiency of services among

things of varying computational capabilities. The FOG (From

cOre to edGe) paradigm will potentially abridge the silos

between personalized and bulk level analytics in SG

informatics. A robust fog topology allows dynamic

augmentation of associated fog nodes, thus significantly

improvising the elasticity and scalability profiles of mission

critical infrastructures. This work outlines the fog computing

paradigm and examines its primacy over the cloud computing

counterpart that became ubiquitous in fulfilling the

computational and analytics needs of a reliable, robust,

resilient and sustainable SG.

The argument here is not to cannibalize the existing cloud

support for SG, but to comprehend the applicability of fog

computing algorithms to interplay with the core centered

cloud computing support leveraged with a new breed of real-

time and latency free utilities. The objective is to assess the

computational viability of FC for SC services (taking smart

grid as use-case) in the realm of IoT space, through proper

orchestration and assignment of compute and storage

resources to the endpoints and where the cloud and fog

technologies tuned to interplay and assist each other in a

synergistic way.

The key contributions of the work are outlined as:

I. Proposed a fog processing architecture customized to

mission critical requirements of Smart Grid.

II. A cost effective resource provisioning optimization model

is proposed to guarantee computational QoS in emerging

Smart Grid.

III. A modified differential evolution (DE) enhanced by fitness

sharing is used for solving the proposed optimization model.

IV. A comparative analysis of both cloud and fog execution

framework is performed to assess and unveil the suitability a

fog aware cloud computing platform over generic cloud

framework.

V. The Significant issues, challenges, and future research

prospects towards fog orchestration of IoT application in

Smart City domains are highlighted.

2. Fog Computing in Smart Grid-A CaseStudy

There exist relentless economic as well as environmental

arguments in the academia, industries, R&Ds and legislative

bodies for the overhaul of the contemporary power grid

comprehended by a full Smart Grid rollout [1]. The latter

integrates green cum renewable energy production utilities,

robust power monitoring schemes, adapts and evolves with the

consumption behavior and requirements. However, the unique

feature that overlays on the heap of a SG amenities is

connectivity and real-time analytics [2]. The recent

advancements in information and communication

infrastructures in general and Internet of Things (IoT) utilities

in specific redefine the notion of ―SMART‖ in current SG

architectures. This work outlines the fog computing paradigm

and examines its primacy over the cloud computing

counterpart that became ubiquitous in fulfilling the

computational and analytics needs of a reliable, robust,

resilient and sustainable SG.

The notion of smartness has been introduced into the

contemporary SG architectures where the local nodes will be

leveraged with computational capabilities. They will no

longer remains a ―thing‖ rather will be transformed into active

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computing nodes or ―objects‖. Every component of SG

network whether it is at the generation, transmission or service

level, will act as active nodes in the entire transportation web.

They are now called object in the sense that they will be

having attributes, gateways, states etc. The whole

transportation system can be encapsulated as a network of

active nodes having deterministic state transitions. This is

achieved through the notion of internet of thing (IoT). IoT

will ensure real-time transport of information to and from the

utilities, smart grid system and other components in the power

system and charging infrastructure in a way that the current as

well as future needs of these entities along with dedicated

business engagement can be determined [3].

Such technologies are enabled by the recent developments

in RFID, smart sensors, communication standards, and

Internet protocols [4]. The basic principle is to have an

environment where smart sensors collaborate directly with the

―objects‖ without human involvement aiming to deliver

multitudes of applications and services [5], [6]. It is a

consensus belief by industries as well as research giants that

down the line, in near future IoT will emerge as a technology

enabler for smart transport, X2X (where X may be any of but

not necessarily same from entities like Vehicles, Grids,

Homes, Micro-grid etc.) data and energy exchange topologies,

optimal renewable integration and intelligent charging

infrastructures [7].

However, it is obvious that in the IoT architecture the

population of connected entities will overshoot the current

growth drift and will jeopardize the normal computing

configurations [8]. This will in turn cause an exponential

escalation in data generation, handling of which is key task to

ensure viable implementation any data aware infrastructure.

Connecting the objects through edge networks and

technologies such as Wireless Sensor Networks (WSN),

Zigbee, bluetooth, RFID, WiFi, 3G, and 4G etc. will increase

the complexity of underlying communication architectures.

Efficient and robust data analytics setup that can establish a

real-time cum intelligent decision making atmosphere at every

edge services becomes the need of hour. An exhaustive review

of existing control paradigms reveal the presence centralized

coordination strategies such as cloud computing, grid

computing etc [8],[9]. However the service demands of IoT

architecture reflect that there needs computing schemes that

can execute locally at the edge itself. The prevalent cloud

models are not intended to handle the seven unprecedented

V’s (Volume, Velocity, Variety, Variability, Veracity,

Visualization and Value) in the data generated by IoT

architectures and coupling the whole universe of ―things‖ or

―objects‖ directly to the cloud is nearly unfeasible [10]. Fog

computing approaches seem to be the preeminent preference

for computations at the extreme edges such as vehicles,

roadways, charging station etc [10]–[13].

However installation of fogs (mini data centers) everywhere

across the edges of the networks and entities may not be cost

productive. The infrastructure demands varying levels of

services which in turn have specified QoS requirements.

Transporting tera-peta bytes of data from millions of edge

devices to the central cloud in real-time is quite infeasible and

even unessential, as a significant percentage of data are

passive and don’t contribute to any decision making process.

Furthermore, there exist several tasks that don’t even entail

storage, processing and analytics at cloud scale. Such

requirements motivate the need of a hybrid control

architecture where the mining and analytics activities are

intelligently dispersed.

The smart grid applications require location aware

geodistributed intelligency in services such as metering

information updates, power thefts, distribution outages,

network intrusions etc, and require prompt and reflex actions

to evolve and organize according to the adversaries. However,

the current smart grid is under immense pressure owing to its

sullen response to the abovementioned computational

demands. Also, due to its fragility concerns in SG control and

coordination sub-systems, repercussions of power outages,

resiliency and reliability issues are growing ever more serious.

Upgrading to a computationally smarter, reliable and resilient

grid has escalated from being a desirable vision, to an urgent

imperative. Here we itemize few but not the least, of some of

the mission critical requirements of an ideal SG infrastructure

plus the sombre experiences encountered while going for pure

cloud computing deployment.

1.1 Support for scalable real-time services: The need of real-time analytics and decisions is being emerged

as the need for the hour to carry up the timing requirements of

mission critical SG utilities [14]. Even if some servers’

fiascos occur, the system should heal itself with just graceful

degradation in latency services. The current cloud models

support for SGs can provide rapid response mechanisms but

adversaries still pose threats to responsiveness.

1.2 Support for scalable, consistency guaranteed,

fault-tolerant services: Consistency for cloud-hosted utilities is a broad term

associated with ACID (Atomicity, Consistency, Isolation and

Durability) guarantees, support for state machine replication,

virtual synchrony, and support for only limited count of node

failures [1]. Today’s smart grid cloud infrastructures often

―embrace inconsistency‖, thus implementing consistency

preserving computational structures constitute a nascent thrust

domain for the research & development sector.

1.3 Privacy and security: The woeful protection services of current cloud

deployments often stimulate the cloud vendors to recapitulate

their security management folks to ―not be evil‖. Stern efforts

are in progress across the power system and transportation

communities to come up with SG cloud utilities and platforms

leveraged with robust protective contrivances where the

stakeholders could entrust the storage of sensitive and critical

data even under concurrent share and access architectures

[15], [16].

1.4 Highly Assured Connectivity: Added with power outages, the smart grid consumers also

experience intermittence in data connectivity. Projects for

establishing mechanisms dedicated to support secured

multipath data routing from user edges to cloud services are on

headway [17],[18]. Critical components of the future smart

grid applications demand better quality of service (QoS) and

quality of experience (QoE) from the data routing backbone

that underlie the cloud-hosted utilities.

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Computational Viability of Fog Methodologies in IoT Enabled Smart City Architectures-A Smart Grid Case Study

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Figure 1: Topology of FOG computing paradigm in a Smart Grid

1.5 Need for risk management modules: Switching from traditional power grid to multi-tenant SG

subsystems introduce substantial risks to power sector, an

issue that need to get fixed in inception phase. The penetration

of autonomous EVs into modern road transport manifolds such

concerns. The EVs are becoming a basin for multi-

dimensional data production, an asset if mishandled, may

befool the execution of whole systems. Moreover, the data

generated due to Cloud-IoT integrated transportation

telematics coupled with advanced metering infrastructures

(AMI) can prove to be harmful to its stakeholders, specifically

for privacy and security [19]. Thus, it’s an earnest need for the

stakeholders to be assured with stringent protection protocols

and be inert from the vulnerabilities. Such scenario

necessitates incorporating robust risk analysis procedures that

will evaluate and quantify the computational and business

risks that persist in such critical infrastructures [20]. Selection

followed by implementation of proper risk analysis paradigms

is itself a full-fledged realm to dwell on. Risks perceived to be

minor in inception phase, later elicits tougher public concerns.

Though the ―pay-for-usage‖ protocols of cloud computing

business models are efficient in satisfying the bulky analytics

and computational tasks, the bliss transforms into worries

when the applications demand null-latency services and when

the data stream chokes the bandwidth restricted

communication buses [21]. The emerging wave IoT based

transportation telematics can prove potentially astonishing in

fulfilling the mobility requirements of contemporary smart

grid architectures [21], [22].

Motivated by the above mentioned mission critical Smart

Grid requirements, the pitfalls associated with current cloud

computing infrastructures to meet such needs, and having the

assumption that the smart grid community is not in a position

to reinvent a remotely owned Internet infrastructure or to

develop computing platforms and elements from scratch, this

work presents a fog computing framework whose principle

underlie on offloading the time and resource critical operations

From cOre to edGe. The argument here is not to cannibalize

the existing cloud support for SG, but to comprehend the

applicability of fog computing algorithms to interplay with the

core centred cloud computing support leveraged with a new

breed of real-time and latency free utilities.

3. Fog Computing Architecture for

Smart Grid This section presents a three schema computing architecture

where the significant portions of smart grid control and

computations are non-trivially hybridized alongside the cloud

computing support. The objective is to overcome the

disruption caused by the development of IoT utilities where

the control, storage, networking and computational needs are

actively proliferated across the edges or end-points.

The lowermost schema namely physical schema or data

generator layer primarily comprises of a wide range of smart

IoT enabled devices which come within the SG domain. For

simplicity, the entities are abstracted into logical clusters of

applications, directly or indirectly influenced by the

expediency of SG operations. The first cluster (C1) represents

vehicular applications where the intelligent vehicles are

arranged to form vehicular fogs. The existing transportation

telematics support such as cellular telephony, on-board

sensors (OBS), roadside units (RSU), and smart wearable

devices will uncover the computational as well as networking

capabilities latent in the underutilized vehicular resources. The

notion is to employ the underutilized vehicular resources into

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communicational and analytics use, where a collaborative

multitude of end-user clients or near-user edge devices carry

out communication and computation, based on better

utilization of individual storage, communication and

computational resources of each vehicle [5]. Similarly, similar

presence of clusters (C2) could also be traced in smart home

networks that have a noteworthy contribution in consistent

operations of the backend SG support. The intelligent IoT

equipped home gadgets such as washing machines, AC,

freezes, parking lots, CC camera etc, are also potentially

active to provide storage, analysis and computational support

for satisfying the prompt and local decision making services.

The third but not the least, cluster C3 depicts similar structure

that can be constituted by utilities involved at the extreme

ends of a SG infrastructure viz. micro-nano grid, PLCs, (HAN,

MAN etc) automated circuit breakers and other entities

associated to diverse range of SG generation, transmission and

distribution services.

The smart nodes within such clusters sense and cultivate the

heterogeneous physical attributes and transmit it to the upper

layers through dedicated edge gateways. However, the whole

or a portion of data generated within these physical clusters

are accumulated at the interim across access points such as

global positioning (GPS), GIS, road-side units (RSU),remote

terminal units (RTU), intelligent electronic devices (IED),

phasor data concentrators (PDC), and other field arrays.

The next tier constitutes the fog computing layer

comprising of intelligent fog devices such as SCADA, smart

meters, routers, switches high-end proxy servers, intelligent

agent and commodity hardware etc, having peculiar ability of

storage, computation and packet routing. The software defined

networking (SDN) assembles the physical clusters to form

virtualized inter cluster private networks (ICPN) that route the

generated data to the fog devices spanned across the fog

computing layer The fog devices and its corresponding

utilities form geographically distributed virtual computing

snapshots or instances that are mapped to lower layer devices

in order serve the processing and computing demands of SG.

4. Networking and System Formulation

In a cloud computing model the mega data center (MDC)

provides sharable resource pool available for on demand use.

Since the MDC are far remote from the generation and query

sites, data migration and service latency gives rise to

infeasibilities for real-time and interactive SG applications and

services. However, in a fog architecture, low power fog

computing nodes (FCN) are deployed at the dedicated edges

of the network to provide platform for SG mobility, real-time

response and geo-distributed intelligence.

Consider a fog architecture customized for SG services

which supports both cloud as well as local fog processing, in

which data and computation are selectively offloaded to either

cloud or fog scale processing guided by an application specific

logic. Without loss of generality, let us assume the sets D , F

, and N represents the set of data centers, fog nodes and

number of consumers having cardinality D, F and N

respectively. An instance of Smart Grid (SG) network can be

modeled as a connected cellular graph of order (N +F) whose

vertices are constituted by data consumer set (N) and FCN set

(F). Let (a

ir ) be the frequency of workload arrival on fog node

i. The FCNs are equipped with set of processing elements (E)

each having service rates

ir . An FCN j is reachable from query

source node k if the former is in the preference list L.

For demonstrating the feasibility of a customized fog

computing architecture in smart grid sub-systems proposed in

section 3, the work utilizes metrics that correlates the

performance of fog computing services to that of traditional

cloud computing paradigms. The geo-distributed micro

datacenters in a typical fog model performs a significant

proportion of local computations on the data produced by data

generators at the schema 1. However, the devices are

leveraged with distributed intelligence, in that depending upon

the degree of services criticality and the types of data, the

righteous decision of whether to offload the data to the cloud

or to the local micro-data centers can be undertaken. For SG

applications, the fog computing framework outperforms its

pure cloud counterparts in respect to metrics like power

consumption, latency and carbon footprint (emission) etc [25].

Figure 2: Probabilistic decision tree depicting the workload

offloading strategy

Consider a pilot SG analytics service to be delivered from the

three tier fog architecture devised in section 3 over a 24 hour

time horizon. Out of volume of data generated in the

whole day, the pre-processing and decision modules deployed

in the first tier offloads 1

into the mega datacenters for

cloud level analytics while distributes 2

to the micro

datacenters for local and instant scale processing and

computations. The uncertainty in the data distributions across

multiple schemas is captured by probability tree depicted in

Figure 2. An ideal fog-cloud framework is leveraged with

robust inferencing logic and intelligent filtering devices to

undertake instant decisions on where to distribute the

produced datasets. The objective of the proposed framework is

to minimize the cost encountered due to power consumption,

latency and emission issues. In case of fog computing

approach, an additional cost term needs to be added due to

communication among the IoT enabled sensors as well as

micro datacenters.

1. Cost profile for Generic Cloud processing:

( ) ( ) ( ) ( )C C C C

F x C v C w C c (1)

Where ( )C

F x , ( )C

C v , ( )C

C w and ( )C

C c represent the

overall cost function, power consumption cost (incurred due to

5

Computational Viability of Fog Methodologies in IoT Enabled Smart City Architectures-A Smart Grid Case Study

EAI Endorsed Transactionson Smart Cities

12 2017 - 02 2018 | Volume 2 | Issue 7 | e3

storage and execution of the data at the mega datacenters),

cost due to latency terms and the cost due to carbon footprint

at the data centers respectively.

1

3

. { .( ) (1 ).( )}( ) .

(1 ).(1 ).( ) .

C C S C S C S C A

C E

C F C A

Vv

V

(2)

Where, C S

, C A

and 3

V represent the power consumed in

cloud storage, cloud storage cum processing and amount of

data that is migrated to the cloud layer through cloud-fog

gateway interface (21-22 in fig 1). E

((USD/KWh)) is the

energy to cost conversion factor.

1

3. (1 ) .(1 ) .

( ) .

1 1 1.

C C F

D C

L

D E E F F C

VV

ww

w w w

(3)

L (USD/Minutes) is the delay to cost conversion factor.

1( ) . . .(1 ) .

C C C GC c V V (4)

and G

represents the gas emissions rate from the data

centers and emission to cost conversion factor.

2. Cost Profile for Fog aware Cloud processing:

( ) ( ) ( ) ( )F F F F

F x C v C w C c (5)

Where ( )F

F , ( )F

C , ( )F

C and ( )F

C represent

the overall cost function, power consumption cost (incurred

due to storage and execution of the data at the micro

datacenters), cost due to latency terms and the cost due to

carbon footprint at the local data centers respectively.

2

3

( ) .(1 ).{ .( )( ) .

(1 ).(1 ).( ) .

C F F A

F

C F F S

VC

V

(6)

where, F A

and F S

represent the energy consumed in fog

processing, fog processing plus cloud storage respectively.

2 3

3

1(1 ) . .( ) .

( ) .1 1

(1 ) .(1 ) . .

F

D E

F L

C F

E F F C

V Vw

w

Vw

(7)

2

3

( ) .(1 )( ) . .

.(1 ) .(1 )

C

F

C

V VC

V

(8)

D Cw ,

D Ew ,

E Fw and

F Cw represent the bandwidth of the

channel linking generators to mega datacenters (when data

demands pure cloud storage or computations), generators to

edge routers, edge routers to fog gateways and fog gateways to

cloud gateways respectively.

3. Optimization model:

In order to assess the viability of proposed fog computing

framework, in this subsection a cost optimization model is

proposed. The objective is to reveal the fact that, if properly

designed, a fog computing framework can circumvent

intricacies prevalent in contemporary cloud computing

paradigms. The following optimization framework captures

the scenario where former outperforms the later in terms

overall performance.

M axim ize m in ( ) m in ( )F

F x F x (9)

Subject to:

1V V V (10)

( ) ( )F

C c C c (11)

( ) ( )F

C w C w (12)

5. Simulations and Results For simulating the topology, the 100 most populated cities

around the world are considered for representing the number

of IoT users and the corresponding geographical coordinates

are used to determine the relative Euclidian distance. The

number of application consumers and the potential data traffic

is assumed to be proportional to the population of Internet

users of the city. The user to fog links allows transmission of

packets of 34 to 64K bytes following Poisson arrival pattern

having 8 byte instruction size. The capacity of user to fog

links and fog to cloud links is assumed as 1Gbps and 10Gbps

respectively. For assessment of system performance against

the network parameters the total population of consumers is

captured in a variable in the range [10000, 100000]. The

number of data centers is considered to be 8 and the pairwise

Euclidian distance is stored in 2D variable ED .

For cost analysis, the cost of 1Gbps and 10Gbps

Gateway router port is kept USD50 each per year while cost of

server is USD 4000 per year. These routers are assumed to

consume electricity at 20W and 40W respectively. Upload rate

is USD 12 per byte while storage cost is kept in the range of

USD 0.45-0.55 per hour. The penalty corresponding to CO2

emission is kept USD 1000 per tons of CO2 emitted.

The formulated optimization model is a multistage,

discrete, nonlinear, constrained mixed-integer programming

problem (MINLP). Usually classical mathematical

programming techniques fail to provide tractable solutions to

such problems. Evolutionary optimization algorithms

specifically meta-heuristic methods such as differential

evolution (DE) [price] are promising approach to solve an

MINLP. DE is a population-based evolutionary optimization

method which had proven to be very simple yet powerful to

solve minimization problems with nonlinear and multi-modal

objective functions. It differs from conventional evolutionary

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algorithms in that instead of having a predefined probability

distribution function (pdf) for mutation process, it utilizes

the differences of randomly sampled pairs of objective

vectors for its mutation process [6]. Such variations will

ensemble the topology of the objective function towards

optimization procedure thus providing more efficacious global

optimization capability. We employed, a modified version of

differential evolution with fitness sharing function of niche

radius () in order to reduce the fitness of similar offspring’s.

The fitness function is given by

( , ) 1 0

0

F f

o th e r w is e

(13)

As in DE the evolution strategy is focused to obtain minimal

optimal value, of the shared fitness is obtained from

1

. ( , )

P S

j

f

j

S f F f

(14)

Where, j

f controls the crossover constant commonly

determined on a case to case basis. In order to guarantee the

fact that the best offspring always appear for next generation,

elitism is employed. Further details are beyond the objective

of this paper. In this section we presented the comparative

result analysis of cloud and fog execution in terms of

performance metrics namely response times (service delay),

electricity consumption and cost of architecture.

We depict the overall latency profile of a fog aware cloud

architecture with a generic cloud execution scenario. The

upload latency, inter-fog communication delay and delay due

to fog to cloud dispatch is abstracted in transmission latency

while the delay caused due to computations and analytics at

VM fog nodes and data center servers is accumulated to

processing latency term. The overall service delay is the linear

sum of transmission and processing latency. For a parameter

defined to be the ratio of data packets dispatched to cloud

core to the number of packets entering into the fog network

through consumer to fog gateways.

FC

F

(15)

The fog-cloud comparison delay statistics is shown in Fig. 3

for .2 5 (three fourth of requests are served within fog

alone). It can be observed that for both the fog as well as cloud

platforms the latency is proportional to the population of data

generators (traffic) and performance of fog aware execution

outperforms the cloud counterparts for every volume of traffic.

In Fig. 4 the electricity consumption pattern in

transmission/dispatch of data bytes, computation (at both fog

and cloud servers) is analysed. It can be observed that with the

rise in the population of service consumers the overall power

consumption show linear growth and is significantly lower

than the conventional cloud framework. The fog aware

framework betters the aggregated electricity consumption over

the cloud computing paradigm by more than 40%.

0 2 4 6 8 10

0

10

20

30

40

50

60

Late

ncie

s (Se

cond

s)

No. of Consumers * 104

Fog transmission latency

Fog Processing latency

Fog Service latency

Cloud transmission latency

Cloud Processing latency

Cloud Service latency

Figure 3: Comparison of Latency Metrics between generic cloud

Vs Fog Assisted Cloud Platforms

0 2 4 6 8 10

0

1000

2000

3000

4000

5000

Cos

t due

to P

ower

Con

sum

ptio

n

No of Consumers (*104

)

Fog Cost due to data transmission

Fog Cost due to computation

Fog Cost due to data Storage

Fog-Net Power consumption cost

Cloud Cost due to data transmission

Cloud Cost due to computation

CloudCost due to data Storage

Cloud-Net Power consumption cost

Figure 4: Comparison of various cost parameters

6. Fog Enabled Smart Cities-Challenges

and Future Directions A typical fog platform is driven by key technology

enablers, for its successful deployment. Since, the field is

relatively immature [23], a large amount of experimentation

needs to be done. The FC platform should be able to provide a

framework that others can use it to test different approaches,

techniques, and algorithms. For instance, there are many ways

to autonomously annotate data with semantics within a fog

gateway. It is not possible to develop one universal approach

or algorithm to annotate data. Thus, a FC platform should be

provide flexibility to the developers to write exertions, support

new ways of annotating data [24].

Also, FC should provide built-in supports for varying

communication standards and application level protocols [21].

Further, providing support for existing data analytics

frameworks is also important. Due to low computational

7

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12 2017 - 02 2018 | Volume 2 | Issue 7 | e3

resources of FC gateways, it is important that these plugins

can be easily removed to avoid resource wastage when not

required in a given fog gateway. Despite we see a large

number IoT cloud platforms in the market in both academia

(e.g., OpenIoT) and industry (e.g., IBM Bluemix, Microsoft

Azure IoT), a fog computing platform that supports all the

those features are yet to be researched and developed [25].

We believe such fog platforms would be greatly beneficial to

the research and industrial communities once available as by

then people can easily test new fog computing related

approaches, techniques and algorithms.

Distributed intelligence will be very critical in

making fog computing platforms successful in building future

smart cities. The main reason is that prompt reactions and best

possible decisions should be achieved in a timely manner to

make future cities smarter, safer and more living-enjoyable.

This requires a large amount of research efforts in putting

distributed intelligence in place properly across smart things,

buildings, fog devices/gateways, and cloud computing

infrastructure in a city. This process will involve many

important aspects, such as available domain-dependent

knowledge/ intelligence, combination of business logic,

engineering processes and government policies, cost-efficient

and computation-efficient and context-/semantics-aware

computing models for real-time decision making, etc [26].

High paced R&D and investments efforts since past

decade have led to the maturity of the cloud based techniques

having efficient frameworks, deployment platforms,

simulation toolkits and business models. However, in context

of fog deployments such efforts though on pace, but still near

its infancy [21], [26]. There may be plenty of literatures

hypothesizing the execution scenario of fog platforms but are

still in concept and simulation phase. Roll-out of fog services

needs to inherit many of the properties of cloud counterparts

and the requirement of deploying computational workloads

on fog computing nodes (FCN) need to be demystified

properly [27]. In addition fog comes with its inherent silos

and raises many questions asking for right and consensus

answer. Some of them may be, where to place a workload,

what are the connection policies, protocols and standards, how

to model/interpret the interaction of/among fog nodes, how to

route the workload etc.

Since IoT enabled smart city services are pervasive in

cyber-physical environments and the complex IoT services are

increasingly composed of sensors, devices, and compute

resources within FC infrastructures, orchestrating such

applications can simplify maintenance and enhance data

security and system reliability [28]. However, efficiently

dealing with dynamic variations and transient operational

behavior of such SC services is a crucial challenge. This

section provides an overview of the core issues, challenges,

and future research directions in orchestration for IoT services

in FC enabled smart cities. In the next sub-section we

highlight the key orchestration challenges in fog-enabled

orchestration for SC applications. Following this, the nascent

research avenues envisioned by such issues and challenges are

also explored.

A. Fog orchestration Challenges for Intelligent Transportation Applications in IoT space

1. ScalabilitySince the heterogeneous sensors and smart devices

employed in SC are designed from multiple IoT manufacturers

and vendors, selecting an optimal device becomes increasingly

intricate while considering customized hardware

configurations and personalized SC requirements. Moreover,

there may be applications can only operate with specific

hardware architectures viz. ARM or Intel etc, and through

wide range of operating systems [29]. Additionally, the SC

applications with stringent security requirements might require

specific hardware and protocols to function. An orchestration

framework need not only to cater to such functional

requirements, it must scale efficiently in the face of

increasingly larger workflows that change dynamically [23],

[30]. The orchestrator must assess whether the assembled

systems, comprised of cloud resources, sensors, and fog

computing nodes (FCN), coupled with geographic

distributions and constraints are capable of provisioning

complex services correctly and efficiently. In particular, the

orchestrator must be able to automatically predict, detect, and

resolve issues pertaining to scalability bottlenecks that could

arise from increased application scale in a customized SC

architecture.

2. Privacy and SecuritySecurity is also a critical issue in building future smart

cities. This mainly refers to security of fog computing

platforms, including potential cyber-attacks to smart things,

fog devices/gateways, and trust and authentication, network

security, and data security, etc [31]. For example, cyber-

attacks to smart things, fog devices/gateways can dysfunction

smart things, fog devices/gateways and pose risks in failure of

providing proper services to the city and making wrong

decisions in reaction to emergencies and disasters. Failure of

ensuring trust and authentication will also put any large-scale

fog computing platforms at risk, potentially leading to

intentional and accidental misbehaviour, criminal activities

and so on. Network security is also of great importance since

network attacks such as jamming attacks, sniffer attacks and

so on can create huge risks in fog computing systems,

potentially leading to chaos of the whole fog computing

systems. Data security will also be critical. Sensitive and/or

valuable data generated from any fog computing platforms

should be kept secure [32].

Since in IoT aided SC like use-cases such as Smart Grid,

smart parking etc, a specific application is composed of

multiple sensors, computer chips and devices, their

deployment in varying different geographic locations result in

increased attack vector of involved objects. Examples of

attack vector may be human-caused sabotage of network

infrastructure, malicious programs provoking data leakage, or

even physical access to devices [33]. Holistic security and

risk assessment procedures are needed to effectively and

dynamically evaluate the security and measure risks, as

evaluating the security of dynamic IoT based application

orchestration become increasingly critical for secure data

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9

placement and processing. The IoT integrated devices for fog

support such as switches, routers and base stations etc, if are

brought to be used as publicly accessible computing edge

nodes, the risk associated by public and private vendors that

own these devices as well as those that will employ these

devices will need revised articulation. Also, the intended

objective of such devices, e.g. an internet router for handling

network traffic, cannot be compromised just because it is

being used as fog node. The fog can be made multi-tenant

only when stringent security protocols are enforced.

3. Dynamic WorkflowsAnother significant characteristic and challenge for IoT

enabled SC applications is their ability to evolve and

dynamically change their workflow composition. This

problem, in the context of software upgrades through FCNs or

the frequent join-leave behavior of network objects, will

change the internal properties and performance, potentially

altering the overall workflow execution pattern. Moreover,

handheld devices used by SC stakeholders inevitably suffer

from software and hardware aging, which will invariably

result in changing workflow behavior and its device properties

(for example, low-battery devices will degrade the data

transmission rate). Furthermore, performance of transportation

applications will change owing to their transient and/or short-

lived behavior within the SC subsystem, including spikes in

resource consumption or big data generation. This leads to a

strong requirement for automatic and intelligent

reconfiguration of the topological structure and assigned

resources within the workflow, and importantly, that of FCNs.

4. ToleranceScaling a fog computing framework in proportion to SC

application demands increases the probability of failure. Some

rare software bugs or hardware faults that don’t manifest at

small scale or in testing environments, such as stragglers, can

have a debilitating effect on system performance and

reliability. At the scale, heterogeneity, and complexity we’re

anticipating, different fault combinations will likely occur. To

address these system failures, developers should incorporate

redundant replications and user-transparent, fault-tolerant

deployment and execution techniques in orchestration design.

B. Future Research Directions The challenges outlined in the above sub-section unlock

several key research directions for successful deployment of

fog supported SC architectures. The research prospects

defined for fog life cycle management can be executed in

three broad phases. In deployment phase, research

opportunities include optimal node selection and routing,

parallel algorithms to handle scalability issues, etc. In runtime

phase, incremental design and analytics, re-engineering,

dynamic orchestration etc, are potential research thrusts for

supporting dynamic QoS monitoring and providing guaranteed

QoE [34]. In the evaluation phase, Big-Data-Driven analytics

(BD2A) and Optimization Algorithms are prime avenues that

need to be explored to improve orchestration quality and

accelerate optimization for problem solving.

Figure 5: Functional elements of a typical fog Orchestrator

1. Opportunities in Deployment Phase:i) Optimal Node-Selection and Routing:

Determining resources and services in cloud paradigms is a

well explored area and easily understood, but exploiting

network edges in decentralized fog settings call for discovery

mechanisms to associate optimal nodes [5],[34],[35]. Resource

discovery in fog computing is not as easy as in both tightly

and loosely coupled distributed environments, and manual

mechanisms are not feasible because of sheer volume of FCNs

available at fog layer. If the SC utility needs to execute

machine learning or Big-Data tasks, resource allocation

strategies also need to cater for datastream of heterogeneous

devices from multiple generations as well as online workloads.

Benchmark algorithms need to be developed for efficient

estimation of FCN’s availability and capability [4]. These

algorithms must allow for seamless augmentation (and

release) of FCNs in the computational workflow at varying

hierarchical levels without added latencies or compromised

QoE. Autonomic node recovery mechanisms needs to be

devised to ensure consistency and reliability in in fault

detection in FCN networked architectures, as existing cloud

based solutions don’t fit to them. Besides, the most potential

research aspect to ponder is workflow partitioning in fog

computing environments. Though numerous task partitioning

techniques, languages and tools have been successfully

implemented for cloud data centers, but research regarding

work apportioning among FCNs is still in concept phase.

Without specifying the capabilities and geo-distribution of

candidate FCNs, automated mechanism for realizing

computation offloading among those nodes is challenging.

Maintaining a ranked list of associated host nodes through

priority aware resource management policies, making

hierarchies or pipelines for sequential offloading of

workloads, developing schedulers for dynamically deploying

segregated tasks to a multiple nodes, algorithms for

parallelization and multitasking of only FCNs, FCNs and data

centers or only data enters etc, are rigorous research hypes in

academia as well as R&D community [35].

Computational Viability of Fog Methodologies in IoT Enabled Smart City Architectures-A Smart Grid Case Study

EAI Endorsed Transactionson Smart Cities

12 2017 - 02 2018 | Volume 2 | Issue 7 | e3

ii) Parallelization Approaches to Manage Scale and

Complexity:

Optimization algorithms or graph-based approaches are

typically time and resource-consuming when applied on a

large scale, and necessitate parallel approaches to accelerate

the optimization process. Recent work provides possible

solutions to leverage an in-memory computing framework to

execute tasks in a cloud infrastructure in parallel. However,

realizing dynamic graph generation and partitioning at runtime

to adapt to the shifting space of possible solutions stemming

from the scale and dynamicity of IoT components remains an

unsolved problem.

iii) Heuristics and Late Calibration:

To ensure near-real-time intervention during IoT application

development, one approach is to use correction mechanisms

that could be applied even when suboptimal solutions are

deployed initially.

To ensure near-real-time intervention during IoT

application development, one approach is to use correction

mechanisms that could be applied even when suboptimal

solutions are deployed initially. For example, in some cases, if

the orchestrator finds a candidate solution that approximately

satisfies the reliability and data transmission requirements, it

can temporarily suspend the search for further optimal

solutions. At runtime, the orchestrator can then continue to

improve decision results with new information and a re-

evaluation of constraints, and use task- and data-migration

approaches to realize workflow redeployment.

Figure 6: The conceptual Framework for Big Data-Driven Analytics and Optimization of Smart City based on Cloud and Fog

Platforms

2. Opportunities in Runtime Phasei) Dynamic Orchestration of Fog Resources:

Apart from the initial placement, all workflow

components dynamically change in response to internal

transformations or abnormal system behavior. IoT applications

are exposed to uncertain environments where execution

variations are commonplace. Because of the degradation of

consumable devices and sensors, capabilities such as security

and reliability that initially were guaranteed will vary,

resulting in the initial workflow being no longer optimal or

even totally invalid. Furthermore, the structural topology

might change according to the task execution progress (that is,

a computation task is finished or evicted) or will be affected

by the execution environment’s evolution. Abnormalities

might occur owing to the variability of combinations of

hardware and software crashes, or data skew across different

management domains of devices due to abnormal data and

request bursting. This will result in unbalanced data

communication and subsequent reduction of application

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11

reliability. Therefore, dynamically orchestrating task

execution and resource reallocation is essential.

ii) Incremental Computation Strategies:

The SC applications may often be choreographed

through workflow or task graphs to assemble different IoT

applications. In some domains, the orchestration is supplied

with a plethora of candidate devices with different

geographical locations and attributes. In some cases,

orchestration would typically be considered too

computationally intensive, as it’s extremely time-consuming

to perform operations including pre-filtering, candidate

selection, and combination calculation while considering all

specified constraints and objectives. Static models and

methods become viable when the application workload and

parallel tasks are known at design time. In contrast, in the

presence of variations and disturbances, orchestration methods

typically rely on incremental scheduling at runtime (rather

than straightforward complete recalculation by rerunning

static methods) to decrease unnecessary computation and

minimize schedule makespan.

iii) QoS-Aware Control and Monitoring Protocols:

To capture the dynamic evolution and variables (such as

dynamic evolution, state transition, and new IoT operations),

we should predefine the quantitative criteria and measuring

approach of dynamic QoS thresholds in terms of latency,

availability, throughput, and so on. These thresholds usually

dictate upper and lower bounds on the metrics as desired at

runtime. In normal setting, complex QoS information

processing methods such as hyper-scale matrix update and

calculation would lead to many scalability issues.

iv) Proactive Decision Making:

Localized regions of self-updates become ubiquitous within

fog environments. The orchestrator should record staged states

and data produced by fog components periodically or in an

event-based manner. This information will form a set of time

series of graphs and facilitate the analysis and proactive

recognition of anomalous events to dynamically determine

such hotspots [36].The data and event streams should be

efficiently transmitted among fog components, so system

outage, appliance failure, or load spikes will rapidly feed back

to the central orchestrator for decision making.

3. Opportunities in Evaluation Phase: Big-Data-Drivenanalytics (BD2A) and Optimization

A typical SC framework congregates the diverse smart

entities into a clique like structure in IoT realm and enables a

bidirectional flow of energy and data among the stakeholders

in order to facilitate the assets optimization. The major data

sources for a data driven SC include SC sensing objects such

as SG, SCADA, Connected Vehicles, On-Board Sensors

(OBS), Road-Side Units (RSU), Traffic sensors and actuators,

GPS devices, smart traffic lights and the web data from

recommender systems, crowdsourcing, feedback modules.

Furthermore, the domain of IoT in SC applications is

extended to numerous geographically distributed devices that

produce multidimensional, high-volume dynamic data streams

requiring a noble mix of real-time analytics and data

aggregation. Figure 15.6 depicts the conceptual framework for

Big Data-Driven Analytics (BD2A) and Optimization of an

intelligent traffic management use-case based on Cloud and

Fog Platforms. The fog orchestration module should employ

efficient data-driven optimization and planning algorithms for

reliable data management across complex IoT aided SC

endpoints. While developing SC applications adhered to fog

computing and making proper trade of such applications

across different layers in the fog environment, the developers

should employ robust optimization procedures that stabilizes

the schema definitions, mappings, all overlapping,

interconnection between layers (if any). In order to reduce

data transmission latencies data processing activities and the

database services may be pipelined. Rather than frequent

triggering of Move-Data actions, use of multiple data- locality

principles (e.g. temporal, spatial etc.) and efficient caching

techniques can distribute or reschedule the computation tasks

of FCNs near the sensors thereby improving the delays. The

data relevant attributes related to QoS parameters such as the

data-generation rate or data-compression ratio can be

customized to adapt to the desired degree of performance and

assigned resources to strike a balance between data quality and

specified response-time targets.

A major challenge is that decision operators are still

computationally time consuming. To tackle this problem,

online machine learning can provision several online training

(such as classification and clustering) and prediction models to

capture the constant evolutionary behavior of each system

element, producing time series of trends to intelligently predict

the required system resource usage, failure occurrence, and

straggler compute tasks, all of which can be learned from

historical data and a history-based optimization (HBO)

procedure. Researchers or developers should investigate these

smart techniques, with corresponding heuristics applied in an

existing decision-making framework to create a continuous

feedback loop. Cloud machine learning offers analysts a set of

data exploration tools and a variety of choices for using

machine learning models and algorithms.

7. Conclusions Due to the improvements in sensing technology and

reduction in costs, sensing capabilities are expected to be

integrated into everyday objects around us. There is a natural

tendency that smart city applications are being built in a

centralized manner. That means all the data collected by

sensors are transferred to a cloud node for knowledge

discovery. However, this is a very inefficient approach from

both computational and communication perspectives. To

address such issues, the FC paradigm has been proposed, as it

brings sustainability to the smart city computations. In this

work, high performance of Fog Computing over generic

cloud computing is evaluated, in terms of metrics viz.

latencies, power consumption etc, through a Smart Grid (SG)

use-case. This paper also provides an overview of the core

issues, challenges, and future research directions in fog-

enabled orchestration for IoT enabled SC applications.

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